Logical reasoning is a core challenge in natural language understanding and a fundamental capability of artificial intelligence, underpinning scientific discovery, mathematical theorem proving, and complex decision-making. Despite the remarkable progress of large language models (LLMs), most current approaches still rely on forward reasoning paradigms, generating step-by-step rationales from premises to conclusions. However, such methods often suffer from redundant inference paths, hallucinated steps, and semantic drift, resulting in inefficient and unreliable reasoning. In this paper, we propose a novel framework, Hypothesis-driven Backward Logical Reasoning (HBLR). The core idea is to integrate confidence-aware symbolic translation with hypothesis-driven backward reasoning. In the translation phase, only high-confidence spans are converted into logical form, such as first-order logic (FOL), while uncertain content remains in natural language. A translation reflection module further ensures semantic fidelity by evaluating symbolic outputs and reverting lossy ones back to text when necessary. In the reasoning phase, HBLR simulates human deductive thinking by assuming the conclusion is true and recursively verifying its premises. A reasoning reflection module further identifies and corrects flawed inference steps, enhancing logical coherence. Extensive experiments on five reasoning benchmarks demonstrate that HBLR consistently outperforms strong baselines in both accuracy and efficiency.

Multisensory stimulation promises in improving older adults’ affective experiences, yet its effectiveness depends on seamless affective congruency across sensory cues. This study investigated how visual, auditory, and kinetics correspondence and congruency shape affective experiences through two experiments. Experiment I examined timbre–color associations, showing that affective alignment strengthens perceived correspondence. Experiment II explored auditory–kinetics synchrony in a cross-modal art system, revealing no significant differences across conditions but indicating that older adults with lower cognitive abilities reported higher pleasure than higher-ability peers. Building on these results, an artificial intelligence (Al)-infused mode was integrated to transform strokes into real-time ink-style artworks, reducing cognitive effort, sustaining engagement. Findings demonstrate that AI enhances positive affect (pleasure, surprise, valence, and arousal) and mitigates negative affect (sadness, anger), with effects maximized by high sensory synchrony, providing compensatory support for users with lower cognitive abilities. These findings inform multisensory system design for older adults’ cognitive and affective needs.

Logical reasoning with large language models (LLMs) has received growing attention. One mainstream approach translates natural language into formal logic and then applies symbolic solvers for deduction. While effective in many tasks, these LLM-based translators often fail to generate consistent symbolic representations when the same concept appears in different linguistic forms. Such inconsistencies break logical coherence and lead to solver errors. However, most existing benchmarks lack this type of linguistic variation, which frequently occurs in real-world text, leaving the problem underexplored. To address this gap, we present SoLT, a benchmark that systematically rewrites reasoning datasets into diverse yet logically equivalent forms across multiple levels. Beyond evaluation, SoLT also provides a general method to enrich any dataset with linguistic diversity while preserving both meaning and logic. To further enhance the stability of LLM-based reasoning, we propose MenTaL, which explicitly guides models to build a concept–symbol mapping table during translation. By linking equivalent expressions to shared symbols, MenTaL maintains consistency and mitigates symbol drift. Experiments on SoLT demonstrate that LLMs indeed suffer from inconsistent symbol mapping under linguistic variation, leading to significant drops in reasoning accuracy. Meanwhile, applying MenTaL brings clear and stable performance improvements across diverse inputs. Overall, our findings reveal that overlooking linguistic diversity hides key weaknesses in LLM-based translators, and our work offers a step toward more reliable logical reasoning in varied real-world scenarios. Our code is available at https://github.com/wufeiwuwoshihua/LinguDiver.

Gamification has demonstrated increasing potential in enhancing public participation in sustainability initiatives. However, it depends on user acceptance and sustained usage. Therefore, a comprehensive understanding of the factors that promote and hinder user engagement in environmental gamification is essential for optimizing such interactive design products and services. This study conducted five focus groups with 11 persistent users and 12 former users of Ant Forest, an environmental gamification in China. Participants discussed the perceived benefits, usage barriers, and concerns arising from their experiences of accepting, continuously using, and (partially) abandoning Ant Forest. Consistent with prior literature, factors related to altruism, egoism, and usability were well recognized and elaborated in our findings. Additionally, we identified factors such as credibility, profit distribution, workload, and habituation, which are critical to user retention. These findings are expected to offer valuable insights for the design, development, optimization, and research in the fields of sustainability and gamification.

Social cues play a critical role in the exchange of information during patient–physician communication. Although previous research has recognized the impact of social cues on patients’ perceptions and behaviors in mobile medical consultations, little is known about how different combinations of social cues affect the interactions. Employing a within-subject factorial design experiment, this study examined the effects of three social cues—empathic cues, interactivity cues, and social validation—on patients’ perceptions of interactivity, social presence, and trust. A total of 103 participants was recruited. The results revealed the various significant impacts of empathic cues, interactivity cues, and social validation on perceived interactivity, and social validation affected trust, as well as the relationships among these perceptions. The findings suggested strategies for integrating various social cues to enhance patients’ positive perceptions, offering potential benefits to patient–physician communication in mobile medical platforms.

Affective robots can elicit psychological responses such as attachment and intimacy, which may help alleviate anxiety and enrich users’ emotional experiences. While such robots can take the form of agents (controlled by algorithms) or avatars (controlled by humans or animals), the differential effects of these embodiment forms on users’ emotional responses remain underexplored, particularly in scenarios where avatars are controlled by animals. In this study, we conducted a Wizard of Oz experiment to compare the emotional experience and anxiety relief provided by a robotic cat under different embodiment forms and interaction modes (unidirectional vs. bidirectional). The results indicate that the avatar embodiment significantly enhances users’ affective experiences, fostering stronger emotional bonds and more effective anxiety relief. However, no significant differences were found between the interaction modes with respect to either anxiety relief or emotional outcomes. These findings offer valuable insights for the design of emotionally engaging embodied intelligent systems in contexts such as emotional companionship and mental health interventions. • A wearable robot prototype enables remote interaction between humans and real cats. • Robot embodiment forms and interaction modes affect users’ anxiety and affective experience. • The avatar form leads to greater anxiety relief and stronger affective experiences. • No significant emotional differences are found between interaction modes.

The interface metaphor was suggested to facilitate a user’s mental model development while navigating, whereas its effectiveness is still unknown among the group of older adults. We developed an experiment to investigate how older adults navigate the mobile interfaces with and without metaphors, by evaluating the possible effects of users’ perceptual speed, task complexity, content similarity, and other related user characteristics in an integrated fashion. Results indicated that the use of interface metaphor could assist in the older adults’ navigation performance, but only for those with a higher level of perceptual speed. Additionally, we found that the older adult’s navigation behavior was significantly influenced by the task complexity, content similarity and the users’ level of technology experience. These findings can help researchers and practitioners to evaluate the effectiveness of interface metaphor on older adults’ mobile navigation behavior by identifying the possible influential factors and interpreting the behind reasons.

Mobile devices are becoming ubiquitous among older adults, but have also caused unprecedented challenges due to the high demands of interaction techniques and changeable design patterns found across various applications. This paper aims to investigate how older adults navigate with mobile interfaces and identify their potential usability challenges while navigating. To do so, we summarised six state-of-the-art mobile interface design patterns and conducted individual usability test and in-depth interview with 22 older adults. Participants were asked to perform 19 navigation tasks that contain these design patterns under realistic usage scenarios. Follow-up interviews were held to collect their detailed comments on usability issues regarding visual design, ease of understanding, and interaction and navigation of the design patterns, as well as their personal experience. The results found that overall older adults were able to navigate contents more effectively than menus and buttons. Participants experienced great challenges in directing their attention to the menus and buttons, understanding the meaning of icons, and interacting with these menu components. In contrast, the content-oriented navigation design performed better in understanding, navigation, and interaction, which could be a promising direction for elderly-friendly mobile application design. Design implications are further discussed for creating an elderly-friendly mobile interface.